Crowley, J.L., Coutaz, J., Grosinger, J. et al. (5 more authors) (Cover date: Jan-March 2023) A Hierarchical Framework for Collaborative Artificial Intelligence. IEEE Pervasive Computing, 22 (1). pp. 9-18. ISSN 1536-1268
Abstract
We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilities provided by lower levels. We review research paradigms at each level, with a description of classical engineering-based approaches and modern alternatives based on machine learning, illustrated with a running example using a hypothetical personal service robot. We discuss cross-cutting issues that occur at all levels, focusing on the problem of communicating and sharing comprehension, the role of explanation and the social nature of collaboration. We conclude with a summary of research challenges and a discussion of the potential for economic and societal impact provided by technologies that enhance human abilities and empower people and society through collaboration with intelligent systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union 825619 |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Feb 2024 17:21 |
Last Modified: | 22 Feb 2024 17:21 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/MPRV.2022.3208321 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209509 |